1. Zijian Wang, Eyuphan Bulut, and Boleslaw K. Szymanski Center for Pervasive Computing and Networking and Department of Computer Science Rensselaer Polytechnic Institute Troy, NY 12180 USA IEEE Globecom 2009 Distributed Target Tracking with Directional Binary Sensor Networks

2. Outline Introduction Network model and assumptions Tracking algorithm Simulation Conclusion

3. Introduction One of the most common and important applications of wireless sensor networks is target tracking Sensor nodes provide just binary information about the target, indicating whether it is present or absent in the sensing range of a node Target tracking usually relies on cooperation between sensing nodes to achieve good results

4. Introduction Most of traditional target tracking approaches used omni-directional binary sensor networks Each sensor can only detect the target presence or absence within its sensing range but can not get any direction information of the target

5. Introduction Authors propose a novel distributed target tracking algorithm using directional binary sensor networks Under the directional binary sensing model, each sensor node’s sensing region is divided into sectors and each node can identify in which sector the target is present or absent

6. Network model The sensor network comprises N nodes placed uniformly randomly over a finite, two-dimensional planar region to be monitored Each node’s sensing region is divided into sectors

7. Assumptions A sensor node knows its location and the locations of its neighbors that are defined as nodes whose sensing range intersects its sensing range Each node has a unique identifier and its sensing region forms a disk The sensing range of each node is identical across the network and each node’s sensing region is divided into the same number of equal size sectors The target moves with velocity that is low relative to the node’s sensing frequency

8. Tracking Algorithm Neighbor sector match 0 1 2 3 x y f a x0 −> 1 y1 x : neighbor lists

9. Tracking Algorithm Neighbor sector match x y f a c d b e x : neighbor lists 0123 x0 −> 1000 y0001

10. Tracking Algorithm Location estimate 0 0 0 x z y a c b d 0123 x0 −> 1000 y0010 z0010 x : neighbor lists

11. Tracking Algorithm Location estimate 0 0 0 x z y a c b d 0123 x0 −> 1000 y0010 z0000 x : neighbor lists

12. Tracking Algorithm Trajectory estimate Each node finds the line (or two or more line sections if the target turns around) y=a ⋅ x+b

13. Trajectory estimate - A weighted line fitting method y=a ⋅ x+b This line when expressed as y=a ⋅ x+b minimizes the metric Q defined as:

14. Simulation 800 × 800 area The number of nodes fixed at 300 Sensing range from 50 to 150 units The velocity of the target was adjusted proportionally to the sensing range, making it constant

15. Simulation

17. Conclusion Authors extend target tracking from the traditional omni-directional binary sensing model to directional binary sensing networks Authors introduced a real-time distributed target tracking algorithm